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Analysis of myocardial infarction using discrete wavelet transform
E S Jayachandran1, Paul Joseph K, R Acharya U
1Department of Electrical Engineering, National Institute of Technology, Calicut, Kerala, India.
Insights
This study introduces a novel wavelet transform method for analyzing electrocardiogram (ECG) signals to detect myocardial infarction (MI), commonly known as a heart attack. The technique accurately distinguishes between normal and MI ECG beats with over 95% accuracy.
Area of Science:
- Biomedical Engineering
- Cardiology
- Signal Processing
Background:
- Myocardial infarction (MI), or heart attack, is a critical condition where heart muscle death results from blocked blood supply.
- Electrocardiogram (ECG) signals provide vital diagnostic information but are susceptible to noise, complicating accurate interpretation.
- Morphological changes in ECG signals, such as ST wave elevation and Q wave changes, are key indicators of cardiac events.
Purpose of the Study:
- To develop and evaluate a signal processing technique for accurate detection of myocardial infarction (MI) from ECG signals.
- To leverage the multiresolution properties of wavelet transformation for enhanced analysis of subtle ECG changes.
- To differentiate between normal and MI ECG beats using energy-entropy characteristics in the wavelet domain.
Main Methods:
- The discrete wavelet transform (DWT) was employed to decompose ECG signals into multiple resolution levels.
- Wavelet domain entropy was computed for both normal and MI ECG signals.
- Energy-entropy characteristics were analyzed and compared for a dataset of 2282 normal and 718 MI beats.
Main Results:
- The discrete wavelet transform effectively decomposed ECG signals, enabling detailed analysis.
- Distinct energy-entropy characteristics were observed between normal and MI ECG beats in the wavelet domain.
- The proposed method achieved a detection accuracy exceeding 95% for distinguishing normal from MI ECG beats.
Conclusions:
- Wavelet transformation is a powerful tool for analyzing subtle changes in noisy ECG signals.
- The energy-entropy analysis in the wavelet domain provides a reliable method for MI detection.
- This approach offers a highly accurate and efficient means for identifying heart attacks from ECG data.
Abstract:
Myocardial infarction (MI), is commonly known as a heart attack, occurs when the blood supply to the portion of the heart is blocked causing some heart cells to die. This information is depicted in the elevated ST wave, increased Q wave amplitude and inverted T wave of the electrocardiogram (ECG) signal. ECG signals are prone to noise during acquisition due to electrode movement, muscle tremor, power line interference and baseline wander. Hence, it becomes difficult to decipher the information about the cardiac state from the morphological changes in the ECG signal. These signals can be analyzed using different signal processing techniques. In this work, we have used multiresolution properties of wavelet transformation because it is suitable tool for interpretation of subtle changes in the ECG signal. We have analyzed the normal and MI ECG signals. ECG signal is decomposed into various resolution levels using the discrete wavelet transform (DWT) method. The entropy in the wavelet domain is computed and the energy-entropy characteristics are compared for 2282 normal and 718 MI beats. Our proposed method is able to detect the normal and MI ECG beat with more than 95% accuracy.
